This invention relates to machine vision analysis of images, and specifically, to methods for finding an object in an image using color images.
Machine vision is a term that generally refers to computer-based analysis of images to provide visual sensory input to industrial processes, such as inspection, automated handling, and process control. Machine vision is performed through the application of digital image processing software on image data acquired by digital imaging equipment, such as CMOS or CCD cameras.
Digital image processing typically includes numerous computations to locate and assess characteristics of image features, including comparisons to known models. Methods for performing such a comparison are generally referred to as pattern location.
One commonly used rudimentary method for pattern location is generally known as blob analysis. In this method, the pixels of a digital image are classified as “object” or “background” using conventionally known segmentation methods to provide a 1-bit monochrome image. Connectivity analysis is performed to group neighboring object pixels, and certain properties can be determined from the grouped regions. These properties, including position, can be compared to a known ideal to determine a location in the image.
An improved pattern location method that has attained widespread use in machine vision applications is normalized correlation. In this method, the full range of greylevels are considered, and a match score is the correlation coefficient between the model and the subset of the image at a given position. The location of the model in the image resulting in the best match score determines a location in the image. Rotation and scale variations can be accommodated by digitally resampling the model at various rotations and scale, then running a normalized correlation.
Geometric pattern matching is a pattern location method used in machine vision that can provide extremely accurate pattern location at sub-pixel resolutions independent of rotation and scale. The model is created from a training image to create feature-based descriptions that can be translated, rotated, and scaled to arbitrary precision much faster than digital image resampling and without pixel grid quantization errors.
Machine vision has been performed traditionally on greyscale images acquired by monochrome, or greyscale cameras. Widespread availability of low-cost greyscale cameras, with the computationally efficient processing of a single channel 8-bit image data has been proven effective in most industrial machine vision applications.
Color image data processing dramatically increases the complexity of machine vision operations. Instead of a single channel 8-bit image data set in a greyscale image, a color image is composed of at least three channels of the image intensity levels for each of the three primary color planes, i.e., RGB (red, green, blue). A full color image is composed of an 8-bit image for each color channel, resulting in a 24-bit image data set.
Industrial grade color digital cameras traditionally employed the use of three greyscale image sensors with a prismatic optical component to produce an image data set for each of the RGB (red, green, ble) components. Current trends in the digital imaging industry are resulting in the availability of lower-cost single-chip color image sensors.
Most machine vision software processing tools can not directly process a multi-channel color image. A conversion process or compression algorithm is necessary to reduce the run-time image to an 8-bit greyscale image. This is commonly performed by processing only one of the three channels of a color image, or linearly combining, or averaging, the intensity of the three color planes into a single greyscale image.
Accordingly, there is a need for a methodology for adapting machine vision tools to provide the capability for performing image data processing on full color spectrum without losing image data by compressing multiple channel color images into greyscale images.